Scope and Limitations

This article, part of our series analyzing the state of AI in 2024, details the scope of our research: the specific domains we investigated and the diverse expertise of the 42 individuals we interviewed. This provides crucial context for the findings presented in subsequent articles.

Key Research Domains

Our investigation spanned a wide range of AI research areas, reflecting the field's breadth and complexity. We combined our interview data with insights from key quantitative research papers to provide a well-rounded perspective. A full list of papers included for review are listed in References.

• Foundation Models:

• Beyond Scale: Limitations in reasoning and context understanding.

• The Evaluation Problem: The difficulty of accurately assessing LLM or LAM performance, including exploration of benchmarks and metrics.

• Synthetic Data's Role: The potential and limitations of LLMs for generating training data.

• Open vs. Closed: The evolving dynamics of open-source and commercial LLM development.

• Specific Applications: Code generation, customer service, and other real-world use cases.

• Computer Vision:

• Synthetic Data's Challenges: The difficulties of using purely synthetic data for training vision models, as highlighted in "Observations on Synthetic Image Distributions with Stable Diffusion" which presented quantitative evidence of performance gaps.

• 3D Advancements: New techniques for creating realistic 3D object and scene representations (e.g., implicit neural representations), referencing papers like "LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals"

• Beyond Recognition: Progress in Optical Character Recognition (OCR), including understanding text layout and context, as discussed in "Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis"

• Real-World Applications: Robotics, autonomous driving, and other areas.

• Data and Datasets: We focused on:

• Acquisition Challenges: The difficulty of obtaining high-quality data.

• Synthetic Data's Promise and Peril: Exploring the benefits and limitations.

• Ethical Considerations: Addressing responsible data collection and use.

• Data Practitioner Perspectives: Understanding workflows and challenges, informed by studies like "Understanding the Dataset Practitioners Behind Large Language Models" which provides quantitative data on dataset creation practices.

• The Data Arms Race: The increasing secrecy and competition.

• Robotics, Agents, and Embodied AI: We explored the significant challenges and emerging opportunities in bringing AI into the physical world, moving beyond digital interfaces to create intelligent agents capable of interacting with and manipulating their environment

• Simulations as a Training Ground: High-fidelity simulations allow researchers to rapidly iterate on algorithms and explore scenarios that would be impractical to replicate in the physical world. This includes creating realistic virtual environments, modeling physical interactions, and simulating sensor data. The use of synthetic data, discussed earlier in the context of computer vision, also extends to robotics, where simulated environments can generate vast amounts of labeled training data.

• Towards Autonomy: The paper "Multimodal Web Navigation with Instruction-Finetuned Foundation Models" provides an example of this, exploring how AI can navigate the web – a complex, dynamic environment – using natural language instructions. While seemingly different, the principles of web navigation (understanding context, planning actions, adapting to changes) have direct relevance to embodied AI.

• Sensory Frontiers: For robots to interact effectively with the real world, they need robust sensory capabilities that go beyond basic vision

• AI for Software Engineering: AI is rapidly transforming the software development lifecycle, impacting everything from code creation to testing and security. Our research touched on several key areas:

• Code Generation and Debugging: LLMs are increasingly capable of generating code snippets, suggesting bug fixes, and even creating entire functions based on natural language descriptions. Tools like GitHub Copilot are a practical example of this trend. Further, "CodeQueries: A Dataset of Semantic Queries over Code" provides a benchmark dataset.

• AI-Assisted Code Review: AI can automate aspects of the code review process, identifying potential errors, suggesting improvements, and ensuring code quality. The paper "Resolving Code Review Comments with ML" explores this area, demonstrating how machine learning can help developers address feedback more efficiently.

• Automated Vulnerability Detection: AI can be used to identify potential security vulnerabilities in code, helping developers build more secure software. This includes techniques like static analysis (examining code without executing it) and dynamic analysis (monitoring code execution to detect anomalies). "AI-powered patching" is also on the frontier.

• Ethics, Bias, and Accessibility: As AI systems become more powerful and pervasive, it's crucial to address the ethical implications and ensure they are developed and used responsibly. Our research considered:

• Bias Mitigation: AI models can inherit and amplify biases present in the data they are trained on, leading to unfair or discriminatory outcomes. The paper "An intentional approach to managing bias in general purpose embedding models" highlights the importance of proactively addressing bias, particularly in sensitive domains like healthcare.

• Accessibility for All: AI systems should be designed to be accessible to all users, including those with disabilities. This includes considering the needs of users with visual impairments, hearing impairments, motor impairments, and cognitive disabilities. The paper "Image Creator and Screen Reader User Perspectives on Alt Text for AI-Generated Images" explores this.

• Societal Impact: We also considered the broader societal implications of AI, including its impact on employment, privacy, and the potential for misuse.

• AI Infrastructure and Resources: The development and deployment of advanced AI systems require significant computational resources and infrastructure. We examined:

  • Compute: The ever-increasing demand for processing power, driven by larger models and more complex tasks.

  • Data Centers: The challenges of building and operating energy-efficient and scalable data centers. Specific issues include power consumption (and the 40% spent on cooling), interconnect limitations, and location constraints.

• Networking: The need for high-bandwidth, low-latency networks to support distributed training and inference.

• Knowledge Representation and Reasoning: A critical area for advancing AI capabilities beyond pattern recognition is to improve how AI represents and reasons about knowledge. We looked at:

• Richer Data Representations: Moving beyond simple data formats to capture more complex relationships and structures. This includes techniques like knowledge graphs, which represent entities and their relationships, and object-centric representations, as explored in "DORSal: Diffusion for Object-centric Representations of Scenes et al."

• Reasoning over Structured Data: Enabling AI to reason effectively with structured data, such as tables, as explored in "Chain-of-Table"

III. Areas for Future Exploration: Expanding the Scope

While our initial research provided a broad overview of the AI landscape, certain areas warrant further investigation and would benefit from additional interviews and analysis. These include:

• Reinforcement Learning (RL): Our interviews touched upon RL only tangentially. A deeper exploration of RL, particularly its applications in robotics and game playing, would be valuable. This would require interviewing researchers and practitioners specializing in RL.

• Specific Vertical Applications: While we covered some applications (e.g., healthcare, software engineering), a more in-depth examination of AI's impact on specific industries (e.g., finance, manufacturing, transportation) would provide a more granular understanding of the challenges and opportunities. This would involve interviewing domain experts within those industries.

• Hardware Advancements: Our research focused primarily on the software and algorithmic aspects of AI. A more thorough investigation of the hardware limitations and advancements (e.g., specialized chips, quantum computing) would be beneficial. This would necessitate interviewing hardware engineers and researchers.

• The Geopolitics of AI: We did not delve deeply into the geopolitical implications of AI development, including the competition between nations for AI dominance and the ethical considerations surrounding the use of AI in warfare. This is a complex and rapidly evolving area that warrants dedicated research.

• Causality: Further research could focus on causal inference.

• AI Safety and Alignment: Deeper investigation into techniques to ensure safe models.

These areas represent potential avenues for future research, building upon the foundation laid by this initial project.

IV. Connecting the Dots: A Holistic Perspective

By combining insights from researchers, founders, and industry experts across these diverse research areas, we aimed to develop a holistic understanding of the AI landscape. This broad scope, combined with the depth of our individual interviews, allowed us to identify cross-cutting themes, non-obvious connections, and critical areas for future investigation. This article serves as a map of the territory we covered, providing essential context for the detailed analyses and findings presented in the subsequent articles in this series.